Predicting the occupancy of unknown environments can provide prior information to help with navigation and exploration tasks. Database-based approaches and deep learning-based approaches are commonly used. To infer unknown information, historical data is stored and used in [1]}, [2]}. Without using a database, [3]} extrapolates the local 2D structure to estimate the potential information gain. With the capability of learning from and adapting to a large amount of historical data, map prediction using deep learning has gained popularity in recent years. 2D occupancy maps are predicted in [4]}, [5]} to estimate the total information or uncertainty for exploration. In [6]}, topological information is predicted in a large-scale underground environment. Egocentric 2D occupancy and semantic maps are predicted in [7]}, [8]} to help the navigation tasks. To achieve safe and fast navigation, [9]} proposed egocentric 3-D occupancy prediction of occluded areas. Most existing work focuses on using 2D structural information to predict the occupancy and estimate the total potential information gain within a bounding box. This approach provides a good estimate of the total information that can be obtained in a large region. However, it overestimates the information, as it cannot infer unseen environment. It also does not help with estimating the information gain at certain viewpoints in a 3-D environment.
